Home-grown Semiconductors Ideal for Quantum Computing

Growing electronic components directly onto a semiconductor block avoids messy, noisy oxidation scattering that slows and impedes electronic operation. A UNSW (Australia) study out this month shows that the resulting high-mobility components are ideal candidates for high-frequency, ultra-small electronic devices, quantum dots, and for qubit applications in quantum computing.

Making computers faster requires ever-smaller transistors, with these electronic components now only a handful of nanometres in size. (There are around 12 billion transistors in the postage-stamp sized central chip of modern smartphones.)

However, in even smaller devices, the channel that the electrons flow through has to be very close to the interface between the semiconductor and the metallic gate used to turn the transistor on and off.  Unavoidable surface oxidation and other surface contaminants cause unwanted scattering of electrons flowing through the channel, and also lead to instabilities and noise that are particularly problematic for quantum devices.

In the new work we create transistors in which an ultra-thin metal gate is grown as part of the semiconductor crystal, preventing problems associated with oxidation of the semiconductor surface,” says lead author Yonatan Ashlea Alava.

We have demonstrated that this new design dramatically reduces unwanted effects from surface imperfections, and show that nanoscale quantum point contacts exhibit significantly lower noise than devices fabricated using conventional approaches,” says Yonatan, who is a FLEET PhD student.

This new all single-crystal design will be ideal for making ultra-small electronic devices, quantum dots, and for qubit applications,” comments group leader Prof Alex Hamilton at UNSW.

Collaborating with wafer growers at Cambridge University, the team at UNSW Sydney showed that the problem associated with surface charge can be eliminated by growing an epitaxial aluminium gate before removing the wafer from the growth chamber.

We confirmed the performance improvement via characterisation measurements in the lab at UNSW,” says co-author Dr Daisy Wang.

The high conductivity in ultra-shallow wafers, and the compatibility of the structure with reproducible nano-device fabrication, suggests that MBE-grown aluminium gated wafers are ideal candidates for making ultra-small electronic devices, quantum dots, and for qubit applications.

Source: https://www.fleet.org.au/

How The Coronavirus Infects Human Cells

Tiny artificial lungs grown in a lab from adult stem cells have allowed scientists to watch how coronavirus infects the lungs in a new ‘major breakthrough‘. Researchers from Duke University and Cambridge University produced artificial lungs in two independent and separate studies to examine the spread of Covid-19. The ‘living lung‘ models minimic the tiny air sacs that take up the oxygen we breathe, known to be where most serious lung damage from the deadly virus takes place.   Having access to the models to test the spread of SAS-CoV-2, the virus responsible for Covid-19, will allow researchers to test potential drugs and gain a better understanding of why some people suffer from the disease worse than others.

In both studies the 3D min-lung models were grown from stem cells that repair the deepest portions of the lungs when SARS-CoV-2 attacks – known as alveolar cells. To date, there have been more than 40 million cases of COVID-19 and almost 1.13 million deaths worldwide. The main target tissues of SARS-CoV-2, especially in patients that develop pneumonia, appear to be alveoli, according to the Cambridge team. They extracted the alveoli cells from donated tissue and reprogrammed them back to their earlierstem cell‘ stage and forced them to grow into self-organising alveolar-like 3D structures that mimic the behaviour of key lung tissue. Dr Joo-Hyeon Lee, co-senior author of the Cambridge paper, said we still know surprisingly little about how SARS-CoV-2 infects the lungs and causes disease.

Representative image of three – dimensional human lung alveolar organoid produced by the Cambridge and Korean researchers to better understand SARS-CoV-2

Our approach has allowed us to grow 3D models of key lung tissue – in a sense, “mini-lungs” – in the lab and study what happens when they become infected.’

Duke researchers took a similar approach. The team, led by Duke cell biologist Purushothama Rao Tata, say their model will allow for hundreds of experiments to be run simultaneously to screen for new drug candidates. ‘This is a versatile model system that allows us to study not only SARS-CoV-2, but any respiratory virus that targets these cells, including influenza,‘ Tata said.

Both teams infected models with a strain of SARS-CoV-2 to better understand who the virus spreads and what happens in the lung cells in response to the disease. The Cambridge team worked with researchers from South Korea to take a sample of the virus from a patient who was infected in January after travelling to Wuhan. Using a combination of fluorescence imaging and single cell genetic analysis, they were able to study how the cells responded to the virus.

When the 3D models were exposed to SARS-CoV-2, the virus began to replicate rapidly, reaching full cellular infection just six hours after infectionReplication enables the virus to spread throughout the body, infecting other cells and tissue, explained the Cambridge research team. Around the same time, the cells began to produce interferonsproteins that act as warning signals to neighbouring cells, telling them to activate their defences. After 48 hours, the interferons triggered the innate immune response – its first line of defence – and the cells started fighting back against infectionSixty hours after infection, a subset of alveolar cells began to disintegrate, leading to cell death and damage to the lung tissue.

Source: https://today.duke.edu/

Teaching a car how to drive itself in 20 minutes

Researchers from Wayve, a company founded by a team from the Cambridge University engineering department, have developed a neural network sophisticated enough to learn how to drive a car in 15 to 20 minutes using nothing but a computer and a single camera. The company showed off its robust deep learning methods last week in a company blog post showcasing the no-frills approach to driverless car development. Where companies like Waymo and Uber are relying on a variety of sensors and custom-built hardware, Wayve is creating the world’s first autonomous vehicles based entirely on reinforcement learning.


The AI powering Wayve’s self-driving system is remarkable for its simplicity. It’s a four layer convolutional neural network (learn about neural networks here) that performs all of its processing on a GPU inside the car. It doesn’t require any cloud connectivity or use pre-loaded mapsWayve’s vehicles are early-stage level five autonomous. There’s a lot of work to be done before Wayve’s AI can drive any car under any circumstances. But the idea that driverless cars will require tens of thousands of dollars worth of extraneous hardware is taking a serious blow in the wake of the company’s amazing deep learning techniques. According to Wayve, these algorithms are only going to get smarter.

Source: https://wayve.ai/